Despite their potential, AI agents have been slow to make an impact on enterprises, and one startup is betting that lack of context is the reason.
Trace, launched as part of Y Combinator’s Summer 2025 offering, is a workflow orchestration startup aiming to fill that gap. The company maps complex enterprise environments and processes to give agents the context they need to scale quickly.
“OpenAI and Anthropic are creating great interns that we can leverage internally,” said Trace CEO Tim Cherkasov, referring to the AI Lab’s tools. “We’re developing managers who know where to put them.”
On Thursday, the London-based company announced it had raised $3 million in seed funding from Y Combinator, Zeno Ventures, Transpose Platform Management, Goodwater Capital, Formosa Capital, and WeFunder. Angel investors Benjamin Bryant and Kevin Moore also invested.
Trace’s system starts by building a knowledge graph from a company’s existing tools (email, Slack, Airtable, and other systems that shape the company’s day-to-day operations). With this context in place, users can ask the system high-level tasks such as “I need to design a new microsite” or “Let’s develop a sales plan for 2027.” Trace then comes back with a step-by-step workflow that delegates some tasks to AI agents and assigns others to human workers. When the system calls an AI agent, it asks for certain data needed to complete a subtask.
The idea is to automate the delicate task of onboarding AI agents, one of the biggest hurdles to actual adoption within enterprises.
With so many companies focused on agent AI, Trace will have a lot of competition. Earlier this week, Anthropic launched its own enterprise agent, which focuses on pre-built plugins for specific department functions. Additionally, many of the workplace productivity services Trace leverages, such as Atlassian’s Jira, have launched their own agents, potentially competing with the startup’s system.
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However, Trace’s founders believe that a knowledge graph approach is the key to success because it allows context engineering to be built deep into the fabric of agent deployment.
“2024 and 2025 were still about prompt engineering. Now we are moving from prompt engineering to context engineering,” said CTO Artur Romanov. “Those who provide the best context at the right time will be the infrastructure for building AI-first companies, and we want to be that infrastructure.”
